The latest medical research on General Practice (Family Medicine)

To provide an online interactive decision aid to facilitate shared decision making in the context of medication choices for patients with type 2 diabetes mellitus (T2DM).

The best available clinical prediction model for patients with T2DM was selected based on a review of guidelines, DynaMed, and UpToDate and a search of PubMed. A list of pharmacotherapeutic options for T2DM was compiled based on a review of guidelines, narrative reviews, and expert opinion. To determine the benefits and harms of each treatment, federated search engines were searched for meta-analyses of randomized controlled trials, supplemented by individual randomized controlled trials for outcomes not reported in meta-analyses.

Approximately 2.1 million Canadians have T2DM, with a resulting increased risk of death, cardiovascular disease, and microvascular outcomes. While more than a dozen medication options are available, decisions regarding these medications are challenging, as patients vary in their preferences. Shared decision making has the potential to individualize these difficult decisions, but the number of diabetes-related outcomes and available treatment options have made this historically impractical. It is within this context that the PEER Diabetes Medication Decision Aid was developed. This decision aid provides patients with personalized 10-year risk estimates for 6 clinically important diabetes-related outcomes. The tool also allows patients to focus on the outcome that matters most to them and to compare the benefits and harms of up to 12 different treatment options. This information is displayed in personalized absolute numbers, along with practical considerations such as cost.

The PEER Diabetes Medication Decision Aid provides a practical tool that can enable patients with T2DM to come to autonomous and well-informed medication decisions.

The COVID-19 pandemic disrupted how primary care patients with chronic pain received care. Our study sought to understand how long-term opioid therapy (LtOT) for chronic pain changed over the course of the pandemic overall and for different demographic subgroups.

We used data from electronic health records of 64 primary care clinics across Washington state and Idaho to identify patients who had a chronic pain diagnosis and were receiving long-term opioid therapy. We defined 10-month periods in 2019 to 2021 as prepandemic, early pandemic and late pandemic and used generalized estimating equations analysis to compare across these time periods and demographic characteristics.

We found a proportional decrease in LtOT for chronic pain in the early months of the pandemic (OR = 0.94, P = .007) followed by an increase late pandemic (OR = 1.08, P = .002). Comparing late pandemic to prepandemic, identifying as Asian or Black, having fewer comorbidities, or living in an urban area were associated with higher likelihood of being prescribed LtOT.

The use of LtOT for chronic pain in primary care has increased from before to after the COVID-19 pandemic with racial/ethnic and geographic disparities. Future research is needed to understand these disparities in LtOT and their effect on patient outcomes.

Unplanned readmissions can be avoided by standardizing and improving the coordination of care after discharge. Telemedicine has been increasingly utilized; however, the quality of this care has not been well studied. Standardized measures can provide an objective comparison of care quality. The purpose of our study was to compare quality performance transitions of care management in the office vs telemedicine.

The Epic SlicerDicer tool was used to compare the percentage of encounters that were completed via telemedicine (video visits); or via in-person for comparison, Chi-squared tests were used.

A total of 13,891 patients met the inclusion criteria during the study time frame. There were 12,846 patients in the office and 1,048 in the telemedicine cohort. The office readmission rate was 11.9% with 1,533 patients out of 12,846 compared with telemedicine with the rate of readmission at 12.1% with 126 patients out of 1,045 patients. The P-value for the Chi-squared test between the prepandemic and study time frame was 0.15 and 0.95, respectively. Demographic comparability was seen.

Telemedicine poses little threat of negatively impacting HEDIS performance and might be as effective as posthospitalization traditional office care transitions of care management.